Product Recommendation Based on Eye Tracking Data Using Fixation Duration

https://doi.org/10.22146/ijitee.58693

Juni Nurma Sari(1*), Lukito Edi Nugroho(2), Paulus Insap Santosa(3), Ridi Ferdiana(4)

(1) Politeknik Caltex Riau
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(4) Universitas Gadjah Mada
(*) Corresponding Author

Abstract


E-commerce can be used to increase companies or sellers’ profits. For consumers, e-commerce can help them shop faster. The weakness of e-commerce is that there is too much product information presented in the catalog which in turn makes consumers confused. The solution is by providing product recommendations. As the development of sensor technology, eye tracker can capture user attention when shopping. The user attention was used as data of consumer interest in the product in the form of fixation duration following the Bojko taxonomy. The fixation duration data was processed into product purchase prediction data to know consumers’ desire to buy the products by using Chandon method. Both data could be used as variables to make product recommendations based on eye tracking data. The implementation of the product recommendations based on eye tracking data was an eye tracking experiment at selvahouse.com which sells hijab and women modest wear. The result was a list of products that have similarities to other products. The product recommendation method used was item-to-item collaborative filtering. The novelty of this research is the use of eye tracking data, namely the fixation duration and product purchase prediction data as variables for product recommendations. Product recommendation that produced by eye tracking data can be solution of product recommendation’s problems, namely sparsity and cold start.

Keywords


E-commerce;Consumer Interest in the Product;Product Purchase Prediction;Eye Tracking;Product Recommendations

Full Text:

PDF


References

M.R. Siddiquee, N. Haider, and R.M. Rahman, “A Fuzzy Based Recommendation System with Collaborative Filtering,” The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), 2014, pp. 1-8.

G. Linden, B. Smith, and J. York, “Amazon.com Recommendation: Item to Item Collaborative Filtering,” IEEE Internet Computing, Vol. 7, No. 1, pp. 76-80, Jan.-Feb. 2003.

X. Zhao, Z. Niu, and W. Chen, “Interest before Liking: Two-Step Recommendation Approaches,” Knowledge-Based System, Vol. 48, No. 1, pp. 46-56, Aug. 2013.

Q. Su and L. Chen, “A Method for Discovering Clusters of E-Commerce Interest Patterns Using Click-Stream Data,” Electronic Commerce Research and Applications, Vol. 14, No. 1, pp. 1-13, Jan. 2015.

D.S. Sisodia, “Augmented Session Similarity Based Framework for Measuring Web User Concern from Web Server Logs,” International Journal on Advanced Science, Engineering and Information Technology, Vol. 7, No. 3, pp. 1007-1013, Jun. 2017.

F. Hafizhelmi, K. Zaman, H. Ali, A.A. Shafie, and Z.I. Rizman, “Efficient Human Motion Detection with Adaptive Background for Vision-Based Security System,” International Journal on Advanced Science, Engineering and Information Technology, Vol. 7, No. 3, pp. 1026-1031, Jun. 2017.

A. Poole and L.J. Ball, “Eye Tracking in Human-Computer Interaction and Usability Research: Current Status and Future Prospects,” in Encyclopedia of Human Computer Interaction, C. Ghaoui, Ed., Pennsylvania, USA: Idea Group Reference, 2006, pp. 211-219.

S. Djamasbi, A. Hall-Phillips, and R.R. Yang, “Search Results Pages and Competition for Attention Theory: An Exploratory Eye-Tracking Study,” in Human Interface and the Management of Information. Information and Interaction Design, S. Yamamoto, Ed., Heidelberg, Germany: Springer, 2013, pp. 576-583.

J. Wehrmeyer, “Eye-Tracking Deaf and Hearing Viewing of Sign Language Interpreted News Broadcasts,” Journal of Eye Movement Research, Vol. 7, No. 1, pp. 1-16, Mar. 2014.

P. Chandon, J.W. Hutchinson, E.T. Bradlow, and S.H. Young, “Measuring the Value of Point-of-Purchase Marketing with Commercial Eye-Tracking Data,” in Visual Marketing: From Attention to Action, M. Wedel and R. Pieters, Eds., Mahwah, New Jersey: Lawrence Erlbaum Associates, 2007, pp. 225-258.

S. Wilson and I. Abel, “So You Want to Get Involved in E-Commerce,” Industrial Marketing Management, Vol. 31, Vol. 2, pp. 85-94, Feb. 2002.

G. Schneider, Electronic Commerce, 9th ed., Boston, USA: Cengage Learning, 2013.

F. Al-Qaed and A. Sutcliffe, “Adaptive Decision Support System (ADSS) for B2C E-Commerce,” Proceedings of the 8th International Conference on Electronic Commerce: The New E-Commerce: Innovations for Conquering Current Barriers, Obstacles and Limitations to Conducting Successful Business on the Internet, 2006, pp. 492-503.

P. Xia, J. Xiao, and S. Chen, “An Application of Recommender System with Mingle-TopN Algorithm on B2B Platform,” 2013 International Conference on Advanced Cloud and Big Data, 2013, pp. 170-176.

P. Liu and L. Hai, “Application of Sequence Alignment Technique to Collaborative Recommendations in E-Commerce,” 2010 International Conference on E-Product E-Service and E-Entertainment, 2010, pp. 1-3.

M.M. Rahman, “Contextual Recommendation System,” Proceeding of the 2013 International Conference on Informatics, Electronics and Vision (ICIEV), 2013, pp. 1-6.

B. Sarwar, G. Karypis, J. Konstan, J. Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms,” Proceedings of the 10th International Conference on World Wide Web, 2001, pp. 285-295.

A. Bojko, Eye Tracking the User Experience: A Practical Guide to Research. New York, USA: Rosefeld Media, 2013.

J.N. Sari, L.E. Nugroho, P.I. Santosa, and R. Ferdiana, “Evaluation of Fixation Duration Accuracy in Determining Selected Product on ECommerce,” 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), 2018, pp. 146-151.

I. Krajbich, D. Lu, C. Camerer, and A. Rangel, “The Attentional Drift-Diffusion Model Extends to Simple Purchasing Decisions,” Frontiers in Psychology, Vol. 3, Jun. 2012.

M. von Boguslawski and P. Mildén, “The Attentional Drift-Diffusion Model for Simple Choice in the Quaternary Case, Measuring the Effect of Permutation of Item Location on Choice Behaviour,” Nov. 14.

J.N. Sari, L.E. Nugroho, P.I. Santosa, and R. Ferdiana, “The Measurement of Consumer Interest and Prediction of Product Selection in E-Commerce Using Eye Tracking Method,” International Journal of Intelligent Engineering and Systems, Vol. 11, No. 1, pp. 30-40, Feb. 2018.



DOI: https://doi.org/10.22146/ijitee.58693

Article Metrics

Abstract views : 1293 | views : 683

Refbacks

  • There are currently no refbacks.




Copyright (c) 2021 IJITEE (International Journal of Information Technology and Electrical Engineering)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

ISSN  : 2550-0554 (online)

Contact :

Department of Electrical engineering and Information Technology, Faculty of Engineering
Universitas Gadjah Mada

Jl. Grafika No 2 Kampus UGM Yogyakarta

+62 (274) 552305

Email : ijitee.ft@ugm.ac.id

----------------------------------------------------------------------------